It is often desired to achieve an accurate alignment of two or more similar image data sets, for example three-dimensional medical image datasets. While relating to the same approximate anatomy, the datasets may reflect differences in time of acquisition, imaging modality, imaging parameters, patient position or motion, contrast agents, disease progression and even patient identity.
There are many benefits of registering such data so that the correspondence between identical or equivalent anatomy is known. Such benefits can include ease of navigation while visualizing the data concurrently; correlating physiological and anatomical information provided by separate imaging modalities, including CT scans taken at different energies; easily locating features of interest in follow-up scans having once identified them in an earlier scan (for example, progression of tumours, vascular plaque and other diseases, and movement of stents); comparing new data against reference data of known characteristics in order to identify specific anatomy; enabling the further step of digital subtraction between contrast-enhanced and non-contrast-enhanced datasets, whereby obscuring data such as bone, vessel calcifications and stents can be removed.
Many approaches to registration of three-dimensional medical image datasets are known. Typically, they can be grouped by the type of transformation of data co-ordinates that they use in order to obtain registration.
A first type of known registration is a rigid registration in which the co-ordinates of data points in one data set are subject to rotation, translation and scaling in order to register the data set to another data set.
A second type of known registration is an affine registration in which the coordinates of data points in one dataset are subject to rotation, translation, scaling and shearing in order to register the dataset to another dataset.
A third type of known registration uses a free-form transformation, in which the coordinates of data points in one datasets are subject to a flexible, free-form deformation in order to register the dataset to another dataset.
Rigid and affine transformations can be defined using a limited number of parameters (up to 9 for rigid, 12 for affine). Freeform transformations may be defined using warpfields. A warpfieid is usually a dense vector field, defining an individual displacement for each voxel in a three-dimensional data set. Freeform transformations may also be defined using other fields or functions, for example using B spline functions or thin plate spline functions.
Usually, a registration algorithm defines a certain similarity measure between two datasets, and then proceeds to try and maximize such measure. In the case of a rigid transformation or an affine transformation, a direct optimization scheme can be deployed. In the case of free-form or other non-rigid registrations, other optimization schemes can be used, for example the Crum-Hill-Hawkes method, or the thin plate spline method.
In general, the optimizing of a similarity measure is one of the final stages of known registration procedures. A substantial amount of pre-processing usually takes place at the beginning of the process, such as image filtering, masking, or cropping. Moreover, many methods operate on a multiscale basis, meaning that the data is subsampled before pre-processing. All these operations can have a noticeable impact on algorithm runtime and memory footprint.
Rigid and affine-based registration procedures tend to be simpler and quicker than freeform registration procedures, but they are often unable to recover complex deformations, such as those that may happen in the internal organs of the body. It is unlikely in most circumstances that a single global rigid or affine transformation will be sufficient to align two scans covering large parts of the human body, for example the heart or the abdomen, to a desired precision.
A variation of the known registration methods mentioned above is a piece-wise registration method, in which a data set is divided into contiguous tiles or cubes of data that span the data set, and the tiles or cubes of data are processed independently.
Each of the known approaches mentioned above, when applied globally to a subject dataset, are often unsuccessful in providing fine-grained local registration needed to align small objects.
In one application, it is desirable to register image datasets representative of the heart region of a patient. The image datasets may, for example, be CT datasets or any other suitable type of datasets. A first data set may, for example, be obtained prior to the injection of intravenous contrast agent, or prior to the contrast agent reaching a region that is the intended subject of the scan, and a second data set may be obtained after the intravenous contrast agent has reached the intended subject region of the scan. Following registration of the first and second datasets, the first dataset can be subtracted from the second dataset to provide a third dataset comprising only contrast-enhanced material, affording an unobstructed view of, for example, the vasculature, which can be useful in diagnosis.
However, problems can arise if blood vessels move or deform during the time interval between the two scans. The resulting large local spatial differences may not be successfully addressed by the overall non-rigid registration algorithm, leading to anomalies in the subtracted dataset.
A particularly conspicuous error of this type occurs when the affected blood vessel contains areas of calcification or stents. Such areas are typically small (a few millimetres in diameter) and have a higher density than intravenous contrast agent, and higher still than ordinary blood or soft tissue. If there are even relatively small inaccuracies in the registration, the areas of calcification or stents can cause the appearance in the subtracted dataset of adjacent areas of anomalously high and low density, which can interfere with diagnostic tasks such as assessment of blood flow.